initial commit

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+
+def create_demo(process):
+    with gr.Blocks() as demo:
+        with gr.Column():
+            prompt = gr.Textbox(label='Prompt')
+            n_prompt = gr.Textbox(
+                label='Negative Prompt',
+                value=
+                'low quality, ugly, disfigured, deformed'
+            )
+
+            run_button = gr.Button('Run')
+        result = gr.Gallery(label='Output',
+                            show_label=False,
+                            elem_id='gallery').style(columns=1, rows=1, preview=True)        
+
+        inputs = [
+            prompt,
+            n_prompt
+        ]
+        prompt.submit(
+            fn=process,
+            inputs=inputs,
+            outputs=result
+        )
+        prompt.submit(
+            fn=process,
+            inputs=inputs,
+            outputs=result
+        )
+
+        run_button.click(
+            fn=process,
+            inputs=inputs,
+            outputs=result
+        )
+    return demo
+
+
+if __name__ == '__main__':
+    from model import Model
+    model = Model()    
+    demo = create_demo(model.process)
+    demo.queue().launch()

model.py

@@ -0,0 +1,38 @@
+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+import PIL.Image
+import numpy as np
+
+class Model:
+    def __init__(self):
+        modelID = "runwayml/stable-diffusion-v1-5"
+        #pipeline = StableDiffusionPipeline.from_pretrained(modelID, torch_dtype=torch.float16)
+        self.pipe = StableDiffusionPipeline.from_pretrained(modelID)
+        #prompt = "a photo of an astronaut riding a horse on mars"
+        #n_prompt = "deformed, disfigured"
+
+    def process(self, 
+                prompt: str, 
+                negative_prompt: str,
+                guidance_scale:int = 7,
+                num_images:int = 1,
+                num_steps:int = 2,
+                ) -> list[PIL.Image.Image]:
+        seed = np.random.randint(0, np.iinfo(np.int64).max)
+        generator = torch.Generator().manual_seed(seed)
+        return self.pipe(prompt=prompt,
+                         negative_prompt=negative_prompt,
+                         guidance_scale=guidance_scale,
+                         num_images_per_prompt=num_images,
+                         num_inference_steps=num_steps,
+                         generator=generator).images        
+
+
+
+
+# image = pipeline(prompt=prompt,
+#                  negative_prompt = n_prompt, 
+#                  num_inference_steps = 2,
+#                  guidance_scale = 7).images
+
+